As marathon runners approach the finish line, well-intentioned sideline supporters often cheer words of encouragement: “Great job. Keep going. You’re almost there.” Yet that last stretch can feel like an eternity, perhaps the most mentally and physically challenging part of the race. When it comes to tapping into business intelligence and artificial intelligence (AI) insights across the entire organization at scale, the marathon analogy rings true, with companies struggling to conquer the elusive “last mile.” So often the data, outputs and even the insights are there, but delivering the right intelligence to the right people at the right time to inform decision making is where organizations get stuck.
Let’s explore what’s needed to operationalize business intelligence insights—driving more value from data to create business efficiencies and better outcomes.
Put data in context to create business intelligence
Data on its own is far from intelligent and can quickly become a burden to organizations, overwhelmed by too much data, data without context or poor data quality. A recent Gartner survey found that 31 percent of executives say “lacking the right type of data” is a “very challenging” data and analysis issue.[1]
Without the right type of data, delivering valuable business intelligence, business insight and real-time analytics to support decision making becomes a near impossible task. The same survey went on to reveal five signs your business users are missing enriched context, if they:
- Frequently switch between screens to perform the analysis needed to decide.
- Consider 99 percent of the information presented irrelevant to their decision.
- Remark that risks and impacts of the decision are invisible or unclear.
- Blame the lack of information provided for negatively impacting business outcomes.
- Describe missed business opportunities due to lack of decision support.
To help understand your content, text mining is one business insights essential, providing much-needed functionality to acquire data from a large variety of structured and unstructured sources, including social media streams and documents stored across content silos. Once content is ingested from various sources, natural language processing and AI are applied to make sense of millions of documents at a time, helping to support contextual decisions.
Uncover sentiment, emotion and intent
When done right, text mining doesn’t stop there, leaning on algorithms to extract additional actionable business insights from unstructured user-generated content. According to Gartner, by 2025 AI for video, audio, vibration, text, emotion and other content analytics will trigger major innovations and transformations in 75 percent of Fortune 500 global enterprises.[2]
Separating subjective and objective statements, understanding the reasons behind positive or negative tonality and detecting underlying emotions, intentions and concerns is a game-changer for organizations. As a result of gaining customer intelligence and insights, employees can deliver more personalized and empathetic experiences, supported by intelligent routing and escalation and quick identification of trends and service issues.
Shift to autonomous analytics
Another way to deliver business insights at scale is to address gaps in consumer-friendly analytics—making AI learnings accessible to business users on a self-service basis. This allows enterprises to utilize advanced and predictive analytics techniques without requiring teams of analytics developers and data scientists.
Data discovery tools that provide drag-and-drop experiences, give employees the ability to explore, interact and analyze business intelligence. By eliminating the need to summon a data scientist, organizations push business intelligence over the finish line, extending the benefits of AI-enriched insights to help drive critical decisions. Plus, by shifting the reporting and analysis workload away from IT and engineering, organizations generate significant time savings and efficiency gains.
Turn business insights into visual insights
Without easy-to-use data discovery tools, it can be challenging to get advanced intelligence from your data—and into the hands of business users, providing a roadblock to widespread scalability. Another way to operationalize business intelligence insights is to turn big data into interactive visualizations.
By integrating and embedding rich, interactive business intelligence reports and dashboards into cloud and legacy applications, users can take advantage of self-service reports, with quick access from any device right within their workflows.
Plus, with advanced data exploration tools, users can focus on just the data that supports their role, choosing to apply advanced analytics techniques for desired visualizations or select from recommended smart visualizations. Without any coding, individuals can drive insights based on data selected, utilizing diagrams, bubble charts, pattern mining, decision trees and other visual assets.
Rely on a foundation for value-driven intelligence
A final hurdle to operationalizing business intelligence is the use of multiple, siloed tools to conduct various AI and analytics processes. Being able to support collaborative and complete decision-making processes across an entire organization requires that users have access to data and insight and tools to navigate the information—regardless of skill set or role.
With a single AI and analytics platform, organizations can reduce the complexity of business intelligence insight, benefitting from comprehensive capabilities to securely scale across the enterprise. Relying on a single source for text mining, data discovery and business intelligence and reporting means that organizations can minimize the effort and expertise needed to deliver AI value and operationalize big-data insights faster.
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[1] Gartner® Use Multistructured Analytics for Complex Business Decisions, David Pidsley, 10 November 2022
GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.
[2] Ibid.
